【摘要】：隨著社會(huì)和科技的快速發(fā)展,人們對(duì)于安全防范的意識(shí)也越來(lái)越高,政府部門對(duì)于人們的安全保障水平也需要快速提高,互聯(lián)網(wǎng)技術(shù),信息技術(shù),視頻壓縮技術(shù)等的快速發(fā)展,使得城域視頻監(jiān)控系統(tǒng)也已經(jīng)應(yīng)用到了人們的日常工作和生活當(dāng)中,視頻監(jiān)控對(duì)于保障人民生活和財(cái)產(chǎn)安全的作用是相當(dāng)巨大的,但隨著時(shí)間的推移,城域視頻監(jiān)控系統(tǒng)產(chǎn)生的數(shù)據(jù)量持續(xù)增長(zhǎng),必須利用新的數(shù)據(jù)存儲(chǔ)技術(shù)、處理分析技術(shù)來(lái)適應(yīng)數(shù)據(jù)量的快速增長(zhǎng)。實(shí)際上不只是城域視頻監(jiān)控系統(tǒng),各行各業(yè)都面臨著大數(shù)據(jù)時(shí)代的到來(lái),大數(shù)據(jù)給很多傳統(tǒng)的處理分析技術(shù)帶來(lái)了很大的挑戰(zhàn),為了能夠適應(yīng)大數(shù)據(jù)時(shí)代的需求,相應(yīng)的數(shù)據(jù)處理分析技術(shù)開(kāi)始快速發(fā)展,城域視頻監(jiān)控系統(tǒng)中同樣需要引入這些大數(shù)據(jù)技術(shù)來(lái)分析處理持續(xù)增長(zhǎng)的數(shù)據(jù)。本文通過(guò)對(duì)大數(shù)據(jù)相關(guān)技術(shù)的應(yīng)用研究,利用云存儲(chǔ)技術(shù)對(duì)城域視頻監(jiān)控系統(tǒng)產(chǎn)生的海量數(shù)據(jù)進(jìn)行存儲(chǔ)及管理,利用數(shù)據(jù)挖掘技術(shù)對(duì)存儲(chǔ)的海量數(shù)據(jù)進(jìn)行深入分析。首先利用MongoDB技術(shù)存儲(chǔ)城域視頻監(jiān)控系統(tǒng)數(shù)據(jù)存儲(chǔ)時(shí)的元數(shù)據(jù)信息,然后利用Hadoop分布式文件系統(tǒng)(HDFS)對(duì)原始數(shù)據(jù)進(jìn)行存儲(chǔ),然后通過(guò)視頻數(shù)據(jù)挖掘技術(shù)對(duì)原始監(jiān)控視頻進(jìn)行挖掘,利用挖掘出的數(shù)據(jù)進(jìn)一步深入分析,主要兩個(gè)方面,一是嫌疑車輛偵查分析,利用視頻監(jiān)控中包含的信息設(shè)計(jì)了一套專家系統(tǒng)對(duì)案件嫌疑車輛進(jìn)行識(shí)別,設(shè)計(jì)了系統(tǒng)知識(shí)庫(kù)中的推理規(guī)則以及推理機(jī)制;二是通過(guò)采用關(guān)聯(lián)規(guī)則的挖掘算法對(duì)道路交通事故成因進(jìn)行分析,并采用信息論中的信息增益來(lái)選取事故的影響主因素。通過(guò)將大數(shù)據(jù)技術(shù)應(yīng)用到城域視頻監(jiān)控系統(tǒng)中,可以更好的保障人民和社會(huì)的安全。
[Abstract]:With the rapid development of society and science and technology, people's awareness of security prevention is becoming higher and higher. The level of safety and security of government departments also needs to be improved rapidly. The rapid development of Internet technology, information technology, video compression technology, etc. The city video surveillance system has also been applied to people's daily work and life. Video surveillance plays a very important role in protecting people's life and property security, but with the passage of time, The amount of data generated by metro video surveillance system continues to grow, so it is necessary to use new data storage technology, processing and analysis technology to adapt to the rapid growth of data volume. In fact, it is not just the video surveillance system in the metropolitan area. All kinds of industries are facing the arrival of big data's era. Big data has brought great challenges to many traditional processing and analysis technologies, in order to be able to adapt to the needs of the era of big data. The corresponding data processing and analysis technology has been developing rapidly. It is also necessary to introduce these big data techniques to analyze and deal with the growing data in the metropolitan video surveillance system. In this paper, we use cloud storage technology to store and manage the mass data generated by the metropolitan video surveillance system through the application research of big data related technology, and use data mining technology to deeply analyze the mass data stored. At first, we use MongoDB technology to store metadata information in the data storage of metro video surveillance system, and then use Hadoop distributed file system (HDFS) to store the original data. Then through the video data mining technology to mine the original surveillance video, using the extracted data to further analyze the two main aspects, one is the suspect vehicle investigation analysis, A set of expert system is designed to identify the suspected vehicle by using the information contained in the video surveillance. The inference rules and reasoning mechanism in the system knowledge base are designed. Secondly, the cause of road traffic accidents is analyzed by mining association rules, and the main factors of accidents are selected by the information gain in information theory. By applying big data technology to the video surveillance system of metropolitan area, the security of people and society can be better guaranteed.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.13;TN948.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張鋒軍;;大數(shù)據(jù)技術(shù)研究綜述[J];通信技術(shù);2014年11期

2 涂新莉;劉波;林偉偉;;大數(shù)據(jù)研究綜述[J];計(jì)算機(jī)應(yīng)用研究;2014年06期

3 宋杰;郭朝鵬;王智;張一川;于戈;Jean-Marc PIERSON;;大數(shù)據(jù)分析的分布式MOLAP技術(shù)[J];軟件學(xué)報(bào);2014年04期

4 何清;李寧;羅文娟;史忠植;;大數(shù)據(jù)下的機(jī)器學(xué)習(xí)算法綜述[J];模式識(shí)別與人工智能;2014年04期

5 張恩;張廣弟;蘭磊;;基于MongoDB的海量空間數(shù)據(jù)存儲(chǔ)和并行[J];地理空間信息;2014年01期

6 張引;陳敏;廖小飛;;大數(shù)據(jù)應(yīng)用的現(xiàn)狀與展望[J];計(jì)算機(jī)研究與發(fā)展;2013年S2期

7 高漢松;肖凌;許德瑋;桑梓勤;;基于云計(jì)算的醫(yī)療大數(shù)據(jù)挖掘平臺(tái)[J];醫(yī)學(xué)信息學(xué)雜志;2013年05期

8 宗群;李光宇;郭萌;;基于故障樹(shù)的電梯故障診斷專家系統(tǒng)設(shè)計(jì)[J];控制工程;2013年02期

9 黃斌;許舒人;蒲衛(wèi);;基于MapReduce的數(shù)據(jù)挖掘平臺(tái)設(shè)計(jì)與實(shí)現(xiàn)[J];計(jì)算機(jī)工程與設(shè)計(jì);2013年02期

10 孟小峰;慈祥;;大數(shù)據(jù)管理:概念、技術(shù)與挑戰(zhàn)[J];計(jì)算機(jī)研究與發(fā)展;2013年01期

相關(guān)碩士學(xué)位論文 前1條

1 黃曉云;基于HDFS的云存儲(chǔ)服務(wù)系統(tǒng)研究[D];大連海事大學(xué);2010年

，

本文編號(hào)：2409057